Overview

Dataset statistics

Number of variables24
Number of observations6743373
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 GiB
Average record size in memory175.0 B

Variable types

DateTime1
Categorical13
Text1
Numeric9

Alerts

dep_airport has a high cardinality: 350 distinct valuesHigh cardinality
dep_cityname has a high cardinality: 344 distinct valuesHigh cardinality
arr_airport has a high cardinality: 350 distinct valuesHigh cardinality
arr_cityname has a high cardinality: 344 distinct valuesHigh cardinality
distance_type is highly imbalanced (64.0%)Imbalance
delay_carrier is highly skewed (γ1 = 22.75351034)Skewed
delay_weather is highly skewed (γ1 = 45.85818526)Skewed
delay_nas is highly skewed (γ1 = 22.64626686)Skewed
delay_security is highly skewed (γ1 = 288.4636637)Skewed
dep_delay has 314586 (4.7%) zerosZeros
arr_delay has 124753 (1.9%) zerosZeros
delay_carrier has 5955483 (88.3%) zerosZeros
delay_weather has 6671374 (98.9%) zerosZeros
delay_nas has 6082588 (90.2%) zerosZeros
delay_security has 6735225 (99.9%) zerosZeros
delay_lastaircraft has 6032255 (89.5%) zerosZeros

Reproduction

Analysis started2024-06-23 17:30:48.083095
Analysis finished2024-06-23 17:32:24.657387
Duration1 minute and 36.57 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Distinct365
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size102.9 MiB
Minimum2023-01-01 00:00:00
Maximum2023-12-31 00:00:00
2024-06-23T20:32:24.782749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:32:24.877958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

day_of_week
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.9 MiB
Friday
1003617 
Thursday
998353 
Monday
996628 
Sunday
984932 
Wednesday
951324 
Other values (2)
1808519 

Length

Max length9
Median length8
Mean length7.116674
Min length6

Characters and Unicode

Total characters47990387
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonday
2nd rowTuesday
3rd rowWednesday
4th rowThursday
5th rowFriday

Common Values

ValueCountFrequency (%)
Friday 1003617
14.9%
Thursday 998353
14.8%
Monday 996628
14.8%
Sunday 984932
14.6%
Wednesday 951324
14.1%
Tuesday 937567
13.9%
Saturday 870952
12.9%

Length

2024-06-23T20:32:24.977622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T20:32:25.069810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
friday 1003617
14.9%
thursday 998353
14.8%
monday 996628
14.8%
sunday 984932
14.6%
wednesday 951324
14.1%
tuesday 937567
13.9%
saturday 870952
12.9%

Most occurring characters

ValueCountFrequency (%)
d 7694697
16.0%
a 7614325
15.9%
y 6743373
14.1%
u 3791804
7.9%
n 2932884
 
6.1%
s 2887244
 
6.0%
r 2872922
 
6.0%
e 2840215
 
5.9%
T 1935920
 
4.0%
S 1855884
 
3.9%
Other values (7) 6821119
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47990387
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 7694697
16.0%
a 7614325
15.9%
y 6743373
14.1%
u 3791804
7.9%
n 2932884
 
6.1%
s 2887244
 
6.0%
r 2872922
 
6.0%
e 2840215
 
5.9%
T 1935920
 
4.0%
S 1855884
 
3.9%
Other values (7) 6821119
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47990387
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 7694697
16.0%
a 7614325
15.9%
y 6743373
14.1%
u 3791804
7.9%
n 2932884
 
6.1%
s 2887244
 
6.0%
r 2872922
 
6.0%
e 2840215
 
5.9%
T 1935920
 
4.0%
S 1855884
 
3.9%
Other values (7) 6821119
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47990387
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 7694697
16.0%
a 7614325
15.9%
y 6743373
14.1%
u 3791804
7.9%
n 2932884
 
6.1%
s 2887244
 
6.0%
r 2872922
 
6.0%
e 2840215
 
5.9%
T 1935920
 
4.0%
S 1855884
 
3.9%
Other values (7) 6821119
14.2%

airline
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.9 MiB
Southwest Airlines Co.
1421229 
Delta Air Lines Inc
972931 
American Airlines Inc.
928056 
United Air Lines Inc.
720031 
Skywest Airlines Inc.
664850 
Other values (10)
2036276 

Length

Max length28
Median length22
Mean length19.99837
Min length12

Characters and Unicode

Total characters134856468
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEndeavor Air
2nd rowEndeavor Air
3rd rowEndeavor Air
4th rowEndeavor Air
5th rowEndeavor Air

Common Values

ValueCountFrequency (%)
Southwest Airlines Co. 1421229
21.1%
Delta Air Lines Inc 972931
14.4%
American Airlines Inc. 928056
13.8%
United Air Lines Inc. 720031
10.7%
Skywest Airlines Inc. 664850
9.9%
Republic Airways 286487
 
4.2%
JetBlue Airways 267915
 
4.0%
Spirit Air Lines 258838
 
3.8%
Alaska Airlines Inc. 242643
 
3.6%
American Eagle Airlines Inc. 224692
 
3.3%
Other values (5) 755701
11.2%

Length

2024-06-23T20:32:25.188396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 4006504
19.0%
airlines 3925843
18.6%
air 2263128
10.7%
lines 1951800
9.3%
southwest 1421229
 
6.7%
co 1421229
 
6.7%
american 1152748
 
5.5%
delta 972931
 
4.6%
united 720031
 
3.4%
skywest 664850
 
3.2%
Other values (11) 2590678
12.3%

Most occurring characters

ValueCountFrequency (%)
i 15745526
11.7%
14347598
 
10.6%
e 12341228
 
9.2%
n 12321555
 
9.1%
s 8760767
 
6.5%
r 8698780
 
6.5%
A 8444261
 
6.3%
l 6149361
 
4.6%
t 6014907
 
4.5%
c 5445739
 
4.0%
Other values (27) 36586746
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134856468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 15745526
11.7%
14347598
 
10.6%
e 12341228
 
9.2%
n 12321555
 
9.1%
s 8760767
 
6.5%
r 8698780
 
6.5%
A 8444261
 
6.3%
l 6149361
 
4.6%
t 6014907
 
4.5%
c 5445739
 
4.0%
Other values (27) 36586746
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134856468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 15745526
11.7%
14347598
 
10.6%
e 12341228
 
9.2%
n 12321555
 
9.1%
s 8760767
 
6.5%
r 8698780
 
6.5%
A 8444261
 
6.3%
l 6149361
 
4.6%
t 6014907
 
4.5%
c 5445739
 
4.0%
Other values (27) 36586746
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134856468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 15745526
11.7%
14347598
 
10.6%
e 12341228
 
9.2%
n 12321555
 
9.1%
s 8760767
 
6.5%
r 8698780
 
6.5%
A 8444261
 
6.3%
l 6149361
 
4.6%
t 6014907
 
4.5%
c 5445739
 
4.0%
Other values (27) 36586746
27.1%
Distinct5963
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size456.6 MiB
2024-06-23T20:32:25.423959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.997429
Min length2

Characters and Unicode

Total characters40442901
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowN605LR
2nd rowN605LR
3rd rowN331PQ
4th rowN906XJ
5th rowN337PQ
ValueCountFrequency (%)
n488ha 3327
 
< 0.1%
n487ha 3315
 
< 0.1%
n486ha 3306
 
< 0.1%
n483ha 3222
 
< 0.1%
n484ha 3221
 
< 0.1%
n485ha 3199
 
< 0.1%
n479ha 3190
 
< 0.1%
n475ha 3160
 
< 0.1%
n495ha 3150
 
< 0.1%
n480ha 3107
 
< 0.1%
Other values (5953) 6711176
99.5%
2024-06-23T20:32:25.682708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 9256833
22.9%
8 2779476
 
6.9%
7 2486716
 
6.1%
2 2477902
 
6.1%
3 2475176
 
6.1%
5 2146874
 
5.3%
1 2140182
 
5.3%
9 2098272
 
5.2%
6 2056950
 
5.1%
4 2047108
 
5.1%
Other values (25) 10477412
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40442901
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 9256833
22.9%
8 2779476
 
6.9%
7 2486716
 
6.1%
2 2477902
 
6.1%
3 2475176
 
6.1%
5 2146874
 
5.3%
1 2140182
 
5.3%
9 2098272
 
5.2%
6 2056950
 
5.1%
4 2047108
 
5.1%
Other values (25) 10477412
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40442901
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 9256833
22.9%
8 2779476
 
6.9%
7 2486716
 
6.1%
2 2477902
 
6.1%
3 2475176
 
6.1%
5 2146874
 
5.3%
1 2140182
 
5.3%
9 2098272
 
5.2%
6 2056950
 
5.1%
4 2047108
 
5.1%
Other values (25) 10477412
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40442901
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 9256833
22.9%
8 2779476
 
6.9%
7 2486716
 
6.1%
2 2477902
 
6.1%
3 2475176
 
6.1%
5 2146874
 
5.3%
1 2140182
 
5.3%
9 2098272
 
5.2%
6 2056950
 
5.1%
4 2047108
 
5.1%
Other values (25) 10477412
25.9%

dep_airport
Categorical

HIGH CARDINALITY 

Distinct350
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.3 MiB
ATL
 
332934
DEN
 
284200
DFW
 
280021
ORD
 
255071
CLT
 
192870
Other values (345)
5398277 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters20230119
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBDL
2nd rowBDL
3rd rowBDL
4th rowBDL
5th rowBDL

Common Values

ValueCountFrequency (%)
ATL 332934
 
4.9%
DEN 284200
 
4.2%
DFW 280021
 
4.2%
ORD 255071
 
3.8%
CLT 192870
 
2.9%
LAX 192259
 
2.9%
LAS 188206
 
2.8%
PHX 175144
 
2.6%
SEA 162441
 
2.4%
MCO 161846
 
2.4%
Other values (340) 4518381
67.0%

Length

2024-06-23T20:32:25.789094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
atl 332934
 
4.9%
den 284200
 
4.2%
dfw 280021
 
4.2%
ord 255071
 
3.8%
clt 192870
 
2.9%
lax 192259
 
2.9%
las 188206
 
2.8%
phx 175144
 
2.6%
sea 162441
 
2.4%
mco 161846
 
2.4%
Other values (340) 4518381
67.0%

Most occurring characters

ValueCountFrequency (%)
A 2310554
 
11.4%
L 1868908
 
9.2%
S 1731880
 
8.6%
D 1586087
 
7.8%
T 1071692
 
5.3%
O 1033702
 
5.1%
C 1021107
 
5.0%
M 905264
 
4.5%
F 835546
 
4.1%
W 789661
 
3.9%
Other values (16) 7075718
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20230119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2310554
 
11.4%
L 1868908
 
9.2%
S 1731880
 
8.6%
D 1586087
 
7.8%
T 1071692
 
5.3%
O 1033702
 
5.1%
C 1021107
 
5.0%
M 905264
 
4.5%
F 835546
 
4.1%
W 789661
 
3.9%
Other values (16) 7075718
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20230119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2310554
 
11.4%
L 1868908
 
9.2%
S 1731880
 
8.6%
D 1586087
 
7.8%
T 1071692
 
5.3%
O 1033702
 
5.1%
C 1021107
 
5.0%
M 905264
 
4.5%
F 835546
 
4.1%
W 789661
 
3.9%
Other values (16) 7075718
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20230119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2310554
 
11.4%
L 1868908
 
9.2%
S 1731880
 
8.6%
D 1586087
 
7.8%
T 1071692
 
5.3%
O 1033702
 
5.1%
C 1021107
 
5.0%
M 905264
 
4.5%
F 835546
 
4.1%
W 789661
 
3.9%
Other values (16) 7075718
35.0%

dep_cityname
Categorical

HIGH CARDINALITY 

Distinct344
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.3 MiB
Chicago, IL
 
338766
Atlanta, GA
 
332934
New York, NY
 
288421
Denver, CO
 
284200
Dallas/Fort Worth, TX
 
280021
Other values (339)
5219031 

Length

Max length34
Median length29
Mean length13.045103
Min length8

Characters and Unicode

Total characters87967995
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHartford, CT
2nd rowHartford, CT
3rd rowHartford, CT
4th rowHartford, CT
5th rowHartford, CT

Common Values

ValueCountFrequency (%)
Chicago, IL 338766
 
5.0%
Atlanta, GA 332934
 
4.9%
New York, NY 288421
 
4.3%
Denver, CO 284200
 
4.2%
Dallas/Fort Worth, TX 280021
 
4.2%
Charlotte, NC 192870
 
2.9%
Los Angeles, CA 192259
 
2.9%
Las Vegas, NV 188206
 
2.8%
Washington, DC 186676
 
2.8%
Phoenix, AZ 180547
 
2.7%
Other values (334) 4278473
63.4%

Length

2024-06-23T20:32:25.868432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 729623
 
4.6%
tx 707547
 
4.5%
fl 600100
 
3.8%
ny 367049
 
2.3%
ga 357006
 
2.3%
san 352215
 
2.2%
il 351390
 
2.2%
chicago 338766
 
2.2%
new 337488
 
2.1%
atlanta 332934
 
2.1%
Other values (418) 11242910
71.5%

Most occurring characters

ValueCountFrequency (%)
8973655
 
10.2%
, 6743373
 
7.7%
a 6726622
 
7.6%
o 4843241
 
5.5%
e 4645969
 
5.3%
n 4315840
 
4.9%
t 4204796
 
4.8%
l 3879735
 
4.4%
i 3337081
 
3.8%
r 3178153
 
3.6%
Other values (47) 37119530
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87967995
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8973655
 
10.2%
, 6743373
 
7.7%
a 6726622
 
7.6%
o 4843241
 
5.5%
e 4645969
 
5.3%
n 4315840
 
4.9%
t 4204796
 
4.8%
l 3879735
 
4.4%
i 3337081
 
3.8%
r 3178153
 
3.6%
Other values (47) 37119530
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87967995
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8973655
 
10.2%
, 6743373
 
7.7%
a 6726622
 
7.6%
o 4843241
 
5.5%
e 4645969
 
5.3%
n 4315840
 
4.9%
t 4204796
 
4.8%
l 3879735
 
4.4%
i 3337081
 
3.8%
r 3178153
 
3.6%
Other values (47) 37119530
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87967995
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8973655
 
10.2%
, 6743373
 
7.7%
a 6726622
 
7.6%
o 4843241
 
5.5%
e 4645969
 
5.3%
n 4315840
 
4.9%
t 4204796
 
4.8%
l 3879735
 
4.4%
i 3337081
 
3.8%
r 3178153
 
3.6%
Other values (47) 37119530
42.2%

deptime_label
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.9 MiB
Morning
2611546 
Afternoon
2363351 
Evening
1557317 
Night
 
211159

Length

Max length9
Median length7
Mean length7.6383132
Min length5

Characters and Unicode

Total characters51507995
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMorning
2nd rowMorning
3rd rowMorning
4th rowMorning
5th rowMorning

Common Values

ValueCountFrequency (%)
Morning 2611546
38.7%
Afternoon 2363351
35.0%
Evening 1557317
23.1%
Night 211159
 
3.1%

Length

2024-06-23T20:32:25.957856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T20:32:26.033867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
morning 2611546
38.7%
afternoon 2363351
35.0%
evening 1557317
23.1%
night 211159
 
3.1%

Most occurring characters

ValueCountFrequency (%)
n 13064428
25.4%
o 7338248
14.2%
r 4974897
 
9.7%
i 4380022
 
8.5%
g 4380022
 
8.5%
e 3920668
 
7.6%
M 2611546
 
5.1%
t 2574510
 
5.0%
A 2363351
 
4.6%
f 2363351
 
4.6%
Other values (4) 3536952
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51507995
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 13064428
25.4%
o 7338248
14.2%
r 4974897
 
9.7%
i 4380022
 
8.5%
g 4380022
 
8.5%
e 3920668
 
7.6%
M 2611546
 
5.1%
t 2574510
 
5.0%
A 2363351
 
4.6%
f 2363351
 
4.6%
Other values (4) 3536952
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51507995
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 13064428
25.4%
o 7338248
14.2%
r 4974897
 
9.7%
i 4380022
 
8.5%
g 4380022
 
8.5%
e 3920668
 
7.6%
M 2611546
 
5.1%
t 2574510
 
5.0%
A 2363351
 
4.6%
f 2363351
 
4.6%
Other values (4) 3536952
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51507995
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 13064428
25.4%
o 7338248
14.2%
r 4974897
 
9.7%
i 4380022
 
8.5%
g 4380022
 
8.5%
e 3920668
 
7.6%
M 2611546
 
5.1%
t 2574510
 
5.0%
A 2363351
 
4.6%
f 2363351
 
4.6%
Other values (4) 3536952
 
6.9%

dep_delay
Real number (ℝ)

ZEROS 

Distinct1854
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.201062
Minimum-99
Maximum4413
Zeros314586
Zeros (%)4.7%
Negative3873030
Negative (%)57.4%
Memory size102.9 MiB
2024-06-23T20:32:26.114422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-10
Q1-5
median-2
Q39
95-th percentile78
Maximum4413
Range4512
Interquartile range (IQR)14

Descriptive statistics

Standard deviation55.079476
Coefficient of variation (CV)4.5143181
Kurtosis268.21142
Mean12.201062
Median Absolute Deviation (MAD)5
Skewness12.011293
Sum82276314
Variance3033.7486
MonotonicityNot monotonic
2024-06-23T20:32:26.200028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 504334
 
7.5%
-4 470786
 
7.0%
-3 455314
 
6.8%
-2 413301
 
6.1%
-6 402405
 
6.0%
-1 369789
 
5.5%
-7 340579
 
5.1%
0 314586
 
4.7%
-8 272730
 
4.0%
-9 202664
 
3.0%
Other values (1844) 2996885
44.4%
ValueCountFrequency (%)
-99 1
 
< 0.1%
-72 1
 
< 0.1%
-68 1
 
< 0.1%
-59 3
< 0.1%
-55 1
 
< 0.1%
-53 1
 
< 0.1%
-52 2
< 0.1%
-51 1
 
< 0.1%
-50 1
 
< 0.1%
-49 1
 
< 0.1%
ValueCountFrequency (%)
4413 1
< 0.1%
3786 1
< 0.1%
3695 1
< 0.1%
3518 1
< 0.1%
3445 1
< 0.1%
3343 1
< 0.1%
3249 1
< 0.1%
3238 1
< 0.1%
3221 1
< 0.1%
3024 1
< 0.1%

dep_delay_tag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size424.4 MiB
0
4187616 
1
2555757 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6743373
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4187616
62.1%
1 2555757
37.9%

Length

2024-06-23T20:32:26.270782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T20:32:26.321793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 4187616
62.1%
1 2555757
37.9%

Most occurring characters

ValueCountFrequency (%)
0 4187616
62.1%
1 2555757
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6743373
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4187616
62.1%
1 2555757
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6743373
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4187616
62.1%
1 2555757
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6743373
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4187616
62.1%
1 2555757
37.9%

dep_delay_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.9 MiB
Low <5min
5409706 
Medium >15min
877998 
Hight >60min
 
455669

Length

Max length13
Median length9
Mean length9.723525
Min length9

Characters and Unicode

Total characters65569356
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow <5min
2nd rowLow <5min
3rd rowLow <5min
4th rowLow <5min
5th rowLow <5min

Common Values

ValueCountFrequency (%)
Low <5min 5409706
80.2%
Medium >15min 877998
 
13.0%
Hight >60min 455669
 
6.8%

Length

2024-06-23T20:32:26.393790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T20:32:26.458572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
low 5409706
40.1%
5min 5409706
40.1%
medium 877998
 
6.5%
15min 877998
 
6.5%
hight 455669
 
3.4%
60min 455669
 
3.4%

Most occurring characters

ValueCountFrequency (%)
i 8077040
12.3%
m 7621371
11.6%
6743373
10.3%
n 6743373
10.3%
5 6287704
9.6%
L 5409706
8.3%
w 5409706
8.3%
< 5409706
8.3%
o 5409706
8.3%
> 1333667
 
2.0%
Other values (11) 7124004
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65569356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 8077040
12.3%
m 7621371
11.6%
6743373
10.3%
n 6743373
10.3%
5 6287704
9.6%
L 5409706
8.3%
w 5409706
8.3%
< 5409706
8.3%
o 5409706
8.3%
> 1333667
 
2.0%
Other values (11) 7124004
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65569356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 8077040
12.3%
m 7621371
11.6%
6743373
10.3%
n 6743373
10.3%
5 6287704
9.6%
L 5409706
8.3%
w 5409706
8.3%
< 5409706
8.3%
o 5409706
8.3%
> 1333667
 
2.0%
Other values (11) 7124004
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65569356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 8077040
12.3%
m 7621371
11.6%
6743373
10.3%
n 6743373
10.3%
5 6287704
9.6%
L 5409706
8.3%
w 5409706
8.3%
< 5409706
8.3%
o 5409706
8.3%
> 1333667
 
2.0%
Other values (11) 7124004
10.9%

arr_airport
Categorical

HIGH CARDINALITY 

Distinct350
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.3 MiB
ATL
 
332939
DEN
 
283563
DFW
 
279729
ORD
 
254775
CLT
 
192910
Other values (345)
5399457 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters20230119
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLGA
2nd rowLGA
3rd rowLGA
4th rowLGA
5th rowLGA

Common Values

ValueCountFrequency (%)
ATL 332939
 
4.9%
DEN 283563
 
4.2%
DFW 279729
 
4.1%
ORD 254775
 
3.8%
CLT 192910
 
2.9%
LAX 192415
 
2.9%
LAS 188243
 
2.8%
PHX 175196
 
2.6%
SEA 162323
 
2.4%
MCO 161373
 
2.4%
Other values (340) 4519907
67.0%

Length

2024-06-23T20:32:26.523173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
atl 332939
 
4.9%
den 283563
 
4.2%
dfw 279729
 
4.1%
ord 254775
 
3.8%
clt 192910
 
2.9%
lax 192415
 
2.9%
las 188243
 
2.8%
phx 175196
 
2.6%
sea 162323
 
2.4%
mco 161373
 
2.4%
Other values (340) 4519907
67.0%

Most occurring characters

ValueCountFrequency (%)
A 2310578
 
11.4%
L 1868986
 
9.2%
S 1733219
 
8.6%
D 1584866
 
7.8%
T 1072284
 
5.3%
O 1032982
 
5.1%
C 1021071
 
5.0%
M 904964
 
4.5%
F 835169
 
4.1%
W 789105
 
3.9%
Other values (16) 7076895
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20230119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2310578
 
11.4%
L 1868986
 
9.2%
S 1733219
 
8.6%
D 1584866
 
7.8%
T 1072284
 
5.3%
O 1032982
 
5.1%
C 1021071
 
5.0%
M 904964
 
4.5%
F 835169
 
4.1%
W 789105
 
3.9%
Other values (16) 7076895
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20230119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2310578
 
11.4%
L 1868986
 
9.2%
S 1733219
 
8.6%
D 1584866
 
7.8%
T 1072284
 
5.3%
O 1032982
 
5.1%
C 1021071
 
5.0%
M 904964
 
4.5%
F 835169
 
4.1%
W 789105
 
3.9%
Other values (16) 7076895
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20230119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2310578
 
11.4%
L 1868986
 
9.2%
S 1733219
 
8.6%
D 1584866
 
7.8%
T 1072284
 
5.3%
O 1032982
 
5.1%
C 1021071
 
5.0%
M 904964
 
4.5%
F 835169
 
4.1%
W 789105
 
3.9%
Other values (16) 7076895
35.0%

arr_cityname
Categorical

HIGH CARDINALITY 

Distinct344
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.3 MiB
Chicago, IL
 
338319
Atlanta, GA
 
332939
New York, NY
 
288152
Denver, CO
 
283563
Dallas/Fort Worth, TX
 
279729
Other values (339)
5220671 

Length

Max length34
Median length29
Mean length13.045968
Min length8

Characters and Unicode

Total characters87973825
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York, NY
2nd rowNew York, NY
3rd rowNew York, NY
4th rowNew York, NY
5th rowNew York, NY

Common Values

ValueCountFrequency (%)
Chicago, IL 338319
 
5.0%
Atlanta, GA 332939
 
4.9%
New York, NY 288152
 
4.3%
Denver, CO 283563
 
4.2%
Dallas/Fort Worth, TX 279729
 
4.1%
Charlotte, NC 192910
 
2.9%
Los Angeles, CA 192415
 
2.9%
Las Vegas, NV 188243
 
2.8%
Washington, DC 186597
 
2.8%
Phoenix, AZ 180608
 
2.7%
Other values (334) 4279898
63.5%

Length

2024-06-23T20:32:26.592217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 730226
 
4.6%
tx 707050
 
4.5%
fl 599542
 
3.8%
ny 366899
 
2.3%
ga 357012
 
2.3%
san 352643
 
2.2%
il 350948
 
2.2%
chicago 338319
 
2.2%
new 337294
 
2.1%
atlanta 332939
 
2.1%
Other values (418) 11245153
71.5%

Most occurring characters

ValueCountFrequency (%)
8974652
 
10.2%
, 6743373
 
7.7%
a 6727590
 
7.6%
o 4842414
 
5.5%
e 4646558
 
5.3%
n 4316941
 
4.9%
t 4205094
 
4.8%
l 3880056
 
4.4%
i 3338091
 
3.8%
r 3176704
 
3.6%
Other values (47) 37122352
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87973825
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8974652
 
10.2%
, 6743373
 
7.7%
a 6727590
 
7.6%
o 4842414
 
5.5%
e 4646558
 
5.3%
n 4316941
 
4.9%
t 4205094
 
4.8%
l 3880056
 
4.4%
i 3338091
 
3.8%
r 3176704
 
3.6%
Other values (47) 37122352
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87973825
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8974652
 
10.2%
, 6743373
 
7.7%
a 6727590
 
7.6%
o 4842414
 
5.5%
e 4646558
 
5.3%
n 4316941
 
4.9%
t 4205094
 
4.8%
l 3880056
 
4.4%
i 3338091
 
3.8%
r 3176704
 
3.6%
Other values (47) 37122352
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87973825
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8974652
 
10.2%
, 6743373
 
7.7%
a 6727590
 
7.6%
o 4842414
 
5.5%
e 4646558
 
5.3%
n 4316941
 
4.9%
t 4205094
 
4.8%
l 3880056
 
4.4%
i 3338091
 
3.8%
r 3176704
 
3.6%
Other values (47) 37122352
42.2%

arr_delay
Real number (ℝ)

ZEROS 

Distinct1880
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6272352
Minimum-119
Maximum4405
Zeros124753
Zeros (%)1.9%
Negative4146092
Negative (%)61.5%
Memory size102.9 MiB
2024-06-23T20:32:26.667863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-119
5-th percentile-27
Q1-15
median-6
Q39
95-th percentile78
Maximum4405
Range4524
Interquartile range (IQR)24

Descriptive statistics

Standard deviation57.079037
Coefficient of variation (CV)8.6127978
Kurtosis235.0407
Mean6.6272352
Median Absolute Deviation (MAD)11
Skewness10.934328
Sum44689919
Variance3258.0165
MonotonicityNot monotonic
2024-06-23T20:32:26.747551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11 189214
 
2.8%
-10 189118
 
2.8%
-12 188072
 
2.8%
-9 185850
 
2.8%
-13 184322
 
2.7%
-8 182424
 
2.7%
-14 179312
 
2.7%
-7 176691
 
2.6%
-15 172027
 
2.6%
-6 170459
 
2.5%
Other values (1870) 4925884
73.0%
ValueCountFrequency (%)
-119 1
 
< 0.1%
-98 1
 
< 0.1%
-97 1
 
< 0.1%
-96 1
 
< 0.1%
-94 1
 
< 0.1%
-92 2
 
< 0.1%
-91 1
 
< 0.1%
-89 1
 
< 0.1%
-88 1
 
< 0.1%
-86 5
< 0.1%
ValueCountFrequency (%)
4405 1
< 0.1%
3795 1
< 0.1%
3680 1
< 0.1%
3502 1
< 0.1%
3424 1
< 0.1%
3337 1
< 0.1%
3246 1
< 0.1%
3241 1
< 0.1%
3237 1
< 0.1%
3063 1
< 0.1%

arr_delay_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.9 MiB
Low <5min
5403696 
Medium >15min
885990 
Hight >60min
 
453687

Length

Max length13
Median length9
Mean length9.7273839
Min length9

Characters and Unicode

Total characters65595378
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow <5min
2nd rowLow <5min
3rd rowLow <5min
4th rowLow <5min
5th rowLow <5min

Common Values

ValueCountFrequency (%)
Low <5min 5403696
80.1%
Medium >15min 885990
 
13.1%
Hight >60min 453687
 
6.7%

Length

2024-06-23T20:32:26.825728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T20:32:26.887250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
low 5403696
40.1%
5min 5403696
40.1%
medium 885990
 
6.6%
15min 885990
 
6.6%
hight 453687
 
3.4%
60min 453687
 
3.4%

Most occurring characters

ValueCountFrequency (%)
i 8083050
12.3%
m 7629363
11.6%
6743373
10.3%
n 6743373
10.3%
5 6289686
9.6%
L 5403696
8.2%
w 5403696
8.2%
< 5403696
8.2%
o 5403696
8.2%
> 1339677
 
2.0%
Other values (11) 7152072
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65595378
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 8083050
12.3%
m 7629363
11.6%
6743373
10.3%
n 6743373
10.3%
5 6289686
9.6%
L 5403696
8.2%
w 5403696
8.2%
< 5403696
8.2%
o 5403696
8.2%
> 1339677
 
2.0%
Other values (11) 7152072
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65595378
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 8083050
12.3%
m 7629363
11.6%
6743373
10.3%
n 6743373
10.3%
5 6289686
9.6%
L 5403696
8.2%
w 5403696
8.2%
< 5403696
8.2%
o 5403696
8.2%
> 1339677
 
2.0%
Other values (11) 7152072
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65595378
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 8083050
12.3%
m 7629363
11.6%
6743373
10.3%
n 6743373
10.3%
5 6289686
9.6%
L 5403696
8.2%
w 5403696
8.2%
< 5403696
8.2%
o 5403696
8.2%
> 1339677
 
2.0%
Other values (11) 7152072
10.9%

flight_duration
Real number (ℝ)

Distinct724
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.2981
Minimum0
Maximum795
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size102.9 MiB
2024-06-23T20:32:26.960640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile58
Q187
median124
Q3171
95-th percentile298
Maximum795
Range795
Interquartile range (IQR)84

Descriptive statistics

Standard deviation72.872159
Coefficient of variation (CV)0.51940947
Kurtosis2.4147338
Mean140.2981
Median Absolute Deviation (MAD)41
Skewness1.3861544
Sum9.4608239 × 108
Variance5310.3516
MonotonicityNot monotonic
2024-06-23T20:32:27.043985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 53005
 
0.8%
82 52585
 
0.8%
79 52293
 
0.8%
80 52180
 
0.8%
83 51943
 
0.8%
78 51745
 
0.8%
84 51543
 
0.8%
77 51196
 
0.8%
85 51030
 
0.8%
76 50814
 
0.8%
Other values (714) 6225039
92.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
15 3
 
< 0.1%
16 9
 
< 0.1%
17 28
 
< 0.1%
18 36
< 0.1%
19 45
< 0.1%
20 49
< 0.1%
21 65
< 0.1%
22 81
< 0.1%
23 73
< 0.1%
ValueCountFrequency (%)
795 1
< 0.1%
759 1
< 0.1%
749 1
< 0.1%
744 1
< 0.1%
742 2
< 0.1%
736 1
< 0.1%
735 2
< 0.1%
734 1
< 0.1%
732 1
< 0.1%
731 1
< 0.1%

distance_type
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.9 MiB
Short Haul >1500Mi
5872128 
Medium Haul <3000Mi
857184 
Long Haul <6000Mi
 
14061

Length

Max length19
Median length18
Mean length18.12503
Min length17

Characters and Unicode

Total characters122223837
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShort Haul >1500Mi
2nd rowShort Haul >1500Mi
3rd rowShort Haul >1500Mi
4th rowShort Haul >1500Mi
5th rowShort Haul >1500Mi

Common Values

ValueCountFrequency (%)
Short Haul >1500Mi 5872128
87.1%
Medium Haul <3000Mi 857184
 
12.7%
Long Haul <6000Mi 14061
 
0.2%

Length

2024-06-23T20:32:27.124001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T20:32:27.211029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
haul 6743373
33.3%
short 5872128
29.0%
1500mi 5872128
29.0%
medium 857184
 
4.2%
3000mi 857184
 
4.2%
long 14061
 
0.1%
6000mi 14061
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 14357991
 
11.7%
13486746
 
11.0%
i 7600557
 
6.2%
M 7600557
 
6.2%
u 7600557
 
6.2%
H 6743373
 
5.5%
a 6743373
 
5.5%
l 6743373
 
5.5%
o 5886189
 
4.8%
S 5872128
 
4.8%
Other values (15) 39588993
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122223837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14357991
 
11.7%
13486746
 
11.0%
i 7600557
 
6.2%
M 7600557
 
6.2%
u 7600557
 
6.2%
H 6743373
 
5.5%
a 6743373
 
5.5%
l 6743373
 
5.5%
o 5886189
 
4.8%
S 5872128
 
4.8%
Other values (15) 39588993
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122223837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14357991
 
11.7%
13486746
 
11.0%
i 7600557
 
6.2%
M 7600557
 
6.2%
u 7600557
 
6.2%
H 6743373
 
5.5%
a 6743373
 
5.5%
l 6743373
 
5.5%
o 5886189
 
4.8%
S 5872128
 
4.8%
Other values (15) 39588993
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122223837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14357991
 
11.7%
13486746
 
11.0%
i 7600557
 
6.2%
M 7600557
 
6.2%
u 7600557
 
6.2%
H 6743373
 
5.5%
a 6743373
 
5.5%
l 6743373
 
5.5%
o 5886189
 
4.8%
S 5872128
 
4.8%
Other values (15) 39588993
32.4%

delay_carrier
Real number (ℝ)

SKEWED  ZEROS 

Distinct1650
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1698273
Minimum0
Maximum3957
Zeros5955483
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size102.9 MiB
2024-06-23T20:32:27.277548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile23
Maximum3957
Range3957
Interquartile range (IQR)0

Descriptive statistics

Standard deviation36.457406
Coefficient of variation (CV)7.0519581
Kurtosis861.83964
Mean5.1698273
Median Absolute Deviation (MAD)0
Skewness22.75351
Sum34862074
Variance1329.1424
MonotonicityNot monotonic
2024-06-23T20:32:27.364371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5955483
88.3%
1 25940
 
0.4%
2 25697
 
0.4%
3 24727
 
0.4%
6 24624
 
0.4%
4 23830
 
0.4%
15 23740
 
0.4%
7 22964
 
0.3%
5 22733
 
0.3%
8 21392
 
0.3%
Other values (1640) 572243
 
8.5%
ValueCountFrequency (%)
0 5955483
88.3%
1 25940
 
0.4%
2 25697
 
0.4%
3 24727
 
0.4%
4 23830
 
0.4%
5 22733
 
0.3%
6 24624
 
0.4%
7 22964
 
0.3%
8 21392
 
0.3%
9 20398
 
0.3%
ValueCountFrequency (%)
3957 1
< 0.1%
3786 1
< 0.1%
3502 1
< 0.1%
3424 1
< 0.1%
3337 1
< 0.1%
3246 1
< 0.1%
3221 1
< 0.1%
3045 1
< 0.1%
3024 1
< 0.1%
2998 1
< 0.1%

delay_weather
Real number (ℝ)

SKEWED  ZEROS 

Distinct1073
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74285391
Minimum0
Maximum1860
Zeros6671374
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size102.9 MiB
2024-06-23T20:32:27.443278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1860
Range1860
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.353961
Coefficient of variation (CV)19.322724
Kurtosis2949.4932
Mean0.74285391
Median Absolute Deviation (MAD)0
Skewness45.858185
Sum5009341
Variance206.0362
MonotonicityNot monotonic
2024-06-23T20:32:27.528131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6671374
98.9%
15 1453
 
< 0.1%
16 1340
 
< 0.1%
6 1325
 
< 0.1%
17 1298
 
< 0.1%
7 1269
 
< 0.1%
18 1258
 
< 0.1%
10 1241
 
< 0.1%
19 1238
 
< 0.1%
8 1226
 
< 0.1%
Other values (1063) 60351
 
0.9%
ValueCountFrequency (%)
0 6671374
98.9%
1 1114
 
< 0.1%
2 1188
 
< 0.1%
3 1168
 
< 0.1%
4 1140
 
< 0.1%
5 1104
 
< 0.1%
6 1325
 
< 0.1%
7 1269
 
< 0.1%
8 1226
 
< 0.1%
9 1183
 
< 0.1%
ValueCountFrequency (%)
1860 1
< 0.1%
1747 1
< 0.1%
1738 1
< 0.1%
1728 1
< 0.1%
1653 1
< 0.1%
1643 1
< 0.1%
1609 1
< 0.1%
1561 1
< 0.1%
1529 1
< 0.1%
1522 1
< 0.1%

delay_nas
Real number (ℝ)

SKEWED  ZEROS 

Distinct837
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5669688
Minimum0
Maximum1708
Zeros6082588
Zeros (%)90.2%
Negative0
Negative (%)0.0%
Memory size102.9 MiB
2024-06-23T20:32:27.600470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16
Maximum1708
Range1708
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.004876
Coefficient of variation (CV)5.8453674
Kurtosis1142.3372
Mean2.5669688
Median Absolute Deviation (MAD)0
Skewness22.646267
Sum17310028
Variance225.14629
MonotonicityNot monotonic
2024-06-23T20:32:27.675283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6082588
90.2%
1 36929
 
0.5%
2 26309
 
0.4%
15 25623
 
0.4%
3 24666
 
0.4%
16 23136
 
0.3%
4 23077
 
0.3%
5 21730
 
0.3%
17 21260
 
0.3%
6 20248
 
0.3%
Other values (827) 437807
 
6.5%
ValueCountFrequency (%)
0 6082588
90.2%
1 36929
 
0.5%
2 26309
 
0.4%
3 24666
 
0.4%
4 23077
 
0.3%
5 21730
 
0.3%
6 20248
 
0.3%
7 19252
 
0.3%
8 18386
 
0.3%
9 17266
 
0.3%
ValueCountFrequency (%)
1708 1
< 0.1%
1660 1
< 0.1%
1651 1
< 0.1%
1515 1
< 0.1%
1487 1
< 0.1%
1421 1
< 0.1%
1409 2
< 0.1%
1407 1
< 0.1%
1402 1
< 0.1%
1401 1
< 0.1%

delay_security
Real number (ℝ)

SKEWED  ZEROS 

Distinct201
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030648905
Minimum0
Maximum1460
Zeros6735225
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size102.9 MiB
2024-06-23T20:32:27.750844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1460
Range1460
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6289268
Coefficient of variation (CV)53.147961
Kurtosis179569.94
Mean0.030648905
Median Absolute Deviation (MAD)0
Skewness288.46366
Sum206677
Variance2.6534026
MonotonicityNot monotonic
2024-06-23T20:32:27.828165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6735225
99.9%
15 333
 
< 0.1%
10 293
 
< 0.1%
16 291
 
< 0.1%
17 283
 
< 0.1%
8 270
 
< 0.1%
7 258
 
< 0.1%
12 257
 
< 0.1%
18 257
 
< 0.1%
9 256
 
< 0.1%
Other values (191) 5650
 
0.1%
ValueCountFrequency (%)
0 6735225
99.9%
1 184
 
< 0.1%
2 182
 
< 0.1%
3 200
 
< 0.1%
4 186
 
< 0.1%
5 243
 
< 0.1%
6 245
 
< 0.1%
7 258
 
< 0.1%
8 270
 
< 0.1%
9 256
 
< 0.1%
ValueCountFrequency (%)
1460 1
< 0.1%
1183 1
< 0.1%
885 1
< 0.1%
808 1
< 0.1%
805 1
< 0.1%
600 1
< 0.1%
581 1
< 0.1%
449 1
< 0.1%
376 1
< 0.1%
373 1
< 0.1%

delay_lastaircraft
Real number (ℝ)

ZEROS 

Distinct1349
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6811338
Minimum0
Maximum3581
Zeros6032255
Zeros (%)89.5%
Negative0
Negative (%)0.0%
Memory size102.9 MiB
2024-06-23T20:32:27.906075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile33
Maximum3581
Range3581
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30.446536
Coefficient of variation (CV)5.359236
Kurtosis537.39645
Mean5.6811338
Median Absolute Deviation (MAD)0
Skewness16.353549
Sum38310004
Variance926.99158
MonotonicityNot monotonic
2024-06-23T20:32:27.979614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6032255
89.5%
15 17684
 
0.3%
16 16691
 
0.2%
17 15950
 
0.2%
18 15240
 
0.2%
19 14258
 
0.2%
20 13982
 
0.2%
21 13288
 
0.2%
14 12685
 
0.2%
22 12380
 
0.2%
Other values (1339) 578960
 
8.6%
ValueCountFrequency (%)
0 6032255
89.5%
1 9092
 
0.1%
2 9550
 
0.1%
3 9331
 
0.1%
4 9511
 
0.1%
5 9753
 
0.1%
6 10693
 
0.2%
7 10528
 
0.2%
8 10884
 
0.2%
9 10977
 
0.2%
ValueCountFrequency (%)
3581 1
< 0.1%
3228 1
< 0.1%
2586 1
< 0.1%
2557 1
< 0.1%
2530 1
< 0.1%
2366 1
< 0.1%
2329 1
< 0.1%
2325 1
< 0.1%
2277 1
< 0.1%
2258 1
< 0.1%

manufacturer
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.9 MiB
BOEING
3122309 
AIRBUS
1981785 
EMBRAER
949742 
CANADAIR REGIONAL JET
689534 
DIAMOND AIRCRAFT
 
3

Length

Max length21
Median length6
Mean length7.6746489
Min length6

Characters and Unicode

Total characters51753020
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCANADAIR REGIONAL JET
2nd rowCANADAIR REGIONAL JET
3rd rowCANADAIR REGIONAL JET
4th rowCANADAIR REGIONAL JET
5th rowCANADAIR REGIONAL JET

Common Values

ValueCountFrequency (%)
BOEING 3122309
46.3%
AIRBUS 1981785
29.4%
EMBRAER 949742
 
14.1%
CANADAIR REGIONAL JET 689534
 
10.2%
DIAMOND AIRCRAFT 3
 
< 0.1%

Length

2024-06-23T20:32:28.063143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T20:32:28.124903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
boeing 3122309
38.4%
airbus 1981785
24.4%
embraer 949742
 
11.7%
canadair 689534
 
8.5%
regional 689534
 
8.5%
jet 689534
 
8.5%
diamond 3
 
< 0.1%
aircraft 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I 6483168
12.5%
E 6400861
12.4%
B 6053836
11.7%
A 5689672
11.0%
R 5260343
10.2%
N 4501380
8.7%
O 3811846
7.4%
G 3811843
7.4%
S 1981785
 
3.8%
U 1981785
 
3.8%
Other values (8) 5776501
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51753020
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 6483168
12.5%
E 6400861
12.4%
B 6053836
11.7%
A 5689672
11.0%
R 5260343
10.2%
N 4501380
8.7%
O 3811846
7.4%
G 3811843
7.4%
S 1981785
 
3.8%
U 1981785
 
3.8%
Other values (8) 5776501
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51753020
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 6483168
12.5%
E 6400861
12.4%
B 6053836
11.7%
A 5689672
11.0%
R 5260343
10.2%
N 4501380
8.7%
O 3811846
7.4%
G 3811843
7.4%
S 1981785
 
3.8%
U 1981785
 
3.8%
Other values (8) 5776501
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51753020
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 6483168
12.5%
E 6400861
12.4%
B 6053836
11.7%
A 5689672
11.0%
R 5260343
10.2%
N 4501380
8.7%
O 3811846
7.4%
G 3811843
7.4%
S 1981785
 
3.8%
U 1981785
 
3.8%
Other values (8) 5776501
11.2%

model
Categorical

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.9 MiB
737 NG
2703483 
170/175
863328 
A320
769278 
A321
704641 
CRJ
689534 
Other values (16)
1013109 

Length

Max length10
Median length7
Mean length5.074076
Min length3

Characters and Unicode

Total characters34216387
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCRJ
2nd rowCRJ
3rd rowCRJ
4th rowCRJ
5th rowCRJ

Common Values

ValueCountFrequency (%)
737 NG 2703483
40.1%
170/175 863328
 
12.8%
A320 769278
 
11.4%
A321 704641
 
10.4%
CRJ 689534
 
10.2%
A319 390607
 
5.8%
717 184919
 
2.7%
757 156147
 
2.3%
A220 98132
 
1.5%
190/195 75262
 
1.1%
Other values (11) 108042
 
1.6%

Length

2024-06-23T20:32:28.191641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
737 2720609
28.8%
ng 2703483
28.6%
170/175 863328
 
9.1%
a320 769309
 
8.1%
a321 704641
 
7.4%
crj 689534
 
7.3%
a319 390607
 
4.1%
717 184919
 
2.0%
757 156147
 
1.7%
a220 98132
 
1.0%
Other values (10) 179723
 
1.9%

Most occurring characters

ValueCountFrequency (%)
7 7999129
23.4%
3 4630530
13.5%
1 3179651
 
9.3%
2717090
 
7.9%
N 2717059
 
7.9%
G 2717059
 
7.9%
A 1995364
 
5.8%
0 1827490
 
5.3%
2 1670214
 
4.9%
5 1118661
 
3.3%
Other values (11) 3644140
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34216387
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 7999129
23.4%
3 4630530
13.5%
1 3179651
 
9.3%
2717090
 
7.9%
N 2717059
 
7.9%
G 2717059
 
7.9%
A 1995364
 
5.8%
0 1827490
 
5.3%
2 1670214
 
4.9%
5 1118661
 
3.3%
Other values (11) 3644140
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34216387
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 7999129
23.4%
3 4630530
13.5%
1 3179651
 
9.3%
2717090
 
7.9%
N 2717059
 
7.9%
G 2717059
 
7.9%
A 1995364
 
5.8%
0 1827490
 
5.3%
2 1670214
 
4.9%
5 1118661
 
3.3%
Other values (11) 3644140
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34216387
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 7999129
23.4%
3 4630530
13.5%
1 3179651
 
9.3%
2717090
 
7.9%
N 2717059
 
7.9%
G 2717059
 
7.9%
A 1995364
 
5.8%
0 1827490
 
5.3%
2 1670214
 
4.9%
5 1118661
 
3.3%
Other values (11) 3644140
10.7%

aicraft_age
Real number (ℝ)

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.480635
Minimum1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.9 MiB
2024-06-23T20:32:30.061769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median12
Q320
95-th percentile25
Maximum57
Range56
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.8914987
Coefficient of variation (CV)0.58539517
Kurtosis-0.73164272
Mean13.480635
Median Absolute Deviation (MAD)6
Skewness0.29683303
Sum90904951
Variance62.275752
MonotonicityNot monotonic
2024-06-23T20:32:30.124151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
8 403055
 
6.0%
5 399397
 
5.9%
23 379335
 
5.6%
10 373103
 
5.5%
7 355229
 
5.3%
9 344486
 
5.1%
6 330744
 
4.9%
2 327330
 
4.9%
24 313636
 
4.7%
18 257437
 
3.8%
Other values (29) 3259621
48.3%
ValueCountFrequency (%)
1 186073
2.8%
2 327330
4.9%
3 169787
2.5%
4 141338
 
2.1%
5 399397
5.9%
6 330744
4.9%
7 355229
5.3%
8 403055
6.0%
9 344486
5.1%
10 373103
5.5%
ValueCountFrequency (%)
57 826
 
< 0.1%
56 1062
 
< 0.1%
48 2360
 
< 0.1%
39 645
 
< 0.1%
38 398
 
< 0.1%
34 9283
 
0.1%
33 18807
0.3%
32 34391
0.5%
31 18053
0.3%
30 27339
0.4%

Interactions

2024-06-23T20:31:51.963032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:20.645125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:24.458616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:28.364227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:32.337813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:36.164177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:40.294818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:44.100454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:47.888151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:52.388006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:21.089416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:24.919629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:28.801625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:32.754046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:36.626516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:40.720870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:44.498927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:48.295875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:52.819976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:21.502469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:25.352081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:29.266342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:33.192813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:37.109153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:41.125225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:44.897419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:48.704859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:53.259696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:21.921902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:25.772151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:29.706854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:33.599922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:37.682728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:41.600478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:45.320301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:49.118698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:53.696202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:22.334837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:26.190761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:30.157355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:34.011225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:38.192175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:42.015363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:45.787762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:49.542315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:54.186831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:22.781197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:26.606858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:30.594010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:34.431778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:38.614784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:42.411395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:46.202482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:50.039025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:54.621285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:23.187133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:27.071605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:31.047387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:34.859689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:39.027355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:42.828495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:46.599382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:50.454131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:55.057636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:23.598822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:27.491141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:31.487817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:35.323280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:39.473120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:43.233656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:47.016049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:50.871365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:55.473943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:24.017715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:27.923146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:31.928456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:35.741480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:39.876136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:43.694250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:47.426187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-23T20:31:51.281297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-06-23T20:31:55.750028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-23T20:32:00.138176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

flightdateday_of_weekairlinetail_numberdep_airportdep_citynamedeptime_labeldep_delaydep_delay_tagdep_delay_typearr_airportarr_citynamearr_delayarr_delay_typeflight_durationdistance_typedelay_carrierdelay_weatherdelay_nasdelay_securitydelay_lastaircraftmanufacturermodelaicraft_age
02023-01-02MondayEndeavor AirN605LRBDLHartford, CTMorning-30Low <5minLGANew York, NY-12Low <5min56Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ16
12023-01-03TuesdayEndeavor AirN605LRBDLHartford, CTMorning-50Low <5minLGANew York, NY-8Low <5min62Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ16
22023-01-04WednesdayEndeavor AirN331PQBDLHartford, CTMorning-50Low <5minLGANew York, NY-21Low <5min49Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ10
32023-01-05ThursdayEndeavor AirN906XJBDLHartford, CTMorning-60Low <5minLGANew York, NY-17Low <5min54Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ17
42023-01-06FridayEndeavor AirN337PQBDLHartford, CTMorning-10Low <5minLGANew York, NY-16Low <5min50Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ10
52023-01-07SaturdayEndeavor AirN336PQBDLHartford, CTMorning-100Low <5minLGANew York, NY-13Low <5min62Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ10
62023-01-14SaturdayEndeavor AirN311PQLGANew York, NYAfternoon-80Low <5minCVGCincinnati, OH-31Low <5min117Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ10
72023-01-21SaturdayEndeavor AirN917XJLGANew York, NYAfternoon-100Low <5minCVGCincinnati, OH-25Low <5min125Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ16
82023-01-28SaturdayEndeavor AirN336PQLGANew York, NYAfternoon-50Low <5minCVGCincinnati, OH-15Low <5min130Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ10
92023-01-09MondayEndeavor AirN491PXLGANew York, NYEvening-70Low <5minBGMBinghamton, NY-3Low <5min63Short Haul >1500Mi00000CANADAIR REGIONAL JETCRJ4
flightdateday_of_weekairlinetail_numberdep_airportdep_citynamedeptime_labeldep_delaydep_delay_tagdep_delay_typearr_airportarr_citynamearr_delayarr_delay_typeflight_durationdistance_typedelay_carrierdelay_weatherdelay_nasdelay_securitydelay_lastaircraftmanufacturermodelaicraft_age
67433942023-12-31SundayJetBlue AirwaysN937JBBOSBoston, MAAfternoon601Medium >15minBUFBuffalo, NY43Medium >15min80Short Haul >1500Mi430000AIRBUSA32110
67433952023-12-31SundayJetBlue AirwaysN945JTSFOSan Francisco, CAMorning-80Low <5minJFKNew York, NY-14Low <5min326Medium Haul <3000Mi00000AIRBUSA32110
67433962023-12-31SundayJetBlue AirwaysN558JBORHWorcester, MAAfternoon-40Low <5minFLLFort Lauderdale, FL-35Low <5min169Short Haul >1500Mi00000AIRBUSA32024
67433972023-12-31SundayJetBlue AirwaysN284JBBOSBoston, MAAfternoon-50Low <5minBWIBaltimore, MD-20Low <5min85Short Haul >1500Mi00000EMBRAER190/19516
67433982023-12-31SundayJetBlue AirwaysN661JBJFKNew York, NYMorning201Medium >15minRSWFort Myers, FL-1Low <5min175Short Haul >1500Mi00000AIRBUSA32017
67433992023-12-31SundayJetBlue AirwaysN903JBSJUSan Juan, PRMorning41Low <5minJFKNew York, NY-33Low <5min219Medium Haul <3000Mi00000AIRBUSA32111
67434002023-12-31SundayJetBlue AirwaysN535JBMCOOrlando, FLEvening1131Hight >60minSJUSan Juan, PR100Hight >60min162Short Haul >1500Mi400096AIRBUSA32022
67434012023-12-31SundayJetBlue AirwaysN354JBPHLPhiladelphia, PAAfternoon-110Low <5minBOSBoston, MA-12Low <5min73Short Haul >1500Mi00000EMBRAER190/19511
67434022023-12-31SundayJetBlue AirwaysN768JBPBIWest Palm Beach/Palm Beach, FLAfternoon-70Low <5minBDLHartford, CT-30Low <5min158Short Haul >1500Mi00000AIRBUSA32015
67434032023-12-31SundayJetBlue AirwaysN547JBBDLHartford, CTMorning-80Low <5minPBIWest Palm Beach/Palm Beach, FL-24Low <5min173Short Haul >1500Mi00000AIRBUSA32022